For delivery of audio/video content to consumers, it is possible to provide personalized audio/video content that is adapted to a user profile or to a profile of a set of users. Typically, a personalized audio/video content comprises base video content, which is the same for a group of users, for example a video corresponding to a movie, and content which is specific to a user or a smaller group of users and which is added when the personalized video content is processed for transmission. The specific content is typically a video frame set. The video frame set may for example be information adapted to a center of interest of a specific user or group of user. The user profile is for example obtained from a database or obtained from one or more data accessing device(s). Providing personalized video content implies taking into account a plurality of aspects such as interests of the user: what activities are appreciated by a user, what does the user like to watch, to buy, etc. When the interests of a given user are similar to the interests of an important number of other users, providing personalized content is easier to scale, as the personalized content may be built once and stored e.g. in cache memory and there is no further need to rebuild personalized content for the given user and the other users as it is sufficient to extract it from the cache location and transmit it to the users.
As it has become easier to acquire more detailed user profile data, there is a possibility to increase the granularity with which personalized audio/video content is adapted to users. This improved adaptation leads however to lesser advantages of caching prebuilt contents and scaling is difficult. For example, when the accessing device comprises an Internet browsing function, cookies are stored in the accessing device by the various sites visited by the user. These cookies comprise information on the user and can be used for building a detailed user profile of the user's interests. Consequently, the probability that many users share the same interests decreases with the precision of the profile.
Consequently, when the interest of a user does not correspond to a cached personalized video content, a content provider has two solutions: building a personalized video content adapted to each user, which is expensive in terms of processing and energy cost, or proposing a default cached content to the users, with the risk that it is not well adapted to the user's domain of interest.
The previous problem is increased when competitors compete for product placement in video frame sets that comprise advertisement placeholders. The competition between companies for obtaining the right to place their advertisement is hard and the situation may change very quickly. This means that at a given moment a first company may have the right to place an advertisement in a given placeholder while one minute later the location of the advertisement can be sold to another company that is offering more.
In cloud computing environments, the price of processing resources and storage resources is highly variable as it depends on scalability, offer/demand and energy cost. Resource cost may thus change with a high frequency based on these parameters.
In such a highly dynamic environment, it is difficult for a content provider to manage take optimized decisions whether to store or recompute personalized content.